Practical Machine Learning

SDS 336, Fall 2026

Instructor: Pratik Patil

Lectures: Tuesdays, Thursdays, 12.30-2pm, WAG 420

Office hours: Tuesdays, Thursdays, 2-3pm, WEL 5.216H

Handy links: Website, Syllabus, Canvas

Schedule

Here is the estimated class schedule. It is subject to change, depending on time and class interests.

Week 01 Aug 24 - Aug 30 Course introduction and Python review slide 01, slide 02 Hw 01 due Fri Aug 28
Week 02 Aug 31 - Sep 06 Unsupervised learning Hw 02 due Fri Sep 04
Week 03 Sep 07 - Sep 13 Regression methods Hw 03 due Fri Sep 11
Week 04 Sep 14 - Sep 20 Classification methods Hw 04 due Fri Sep 18
Week 05 Sep 21 - Sep 27 Flexible predictive modeling Hw 05 due Fri Sep 25
Week 06 Sep 28 - Oct 04 Model evaluation and validation Hw 06 due Fri Oct 02
Week 07 Oct 05 - Oct 11 Predictive performance and uncertainty Hw 07 due Fri Oct 09
Week 08 Oct 12 - Oct 18 Midterm exam and mid project presentations Hw 08 due Fri Oct 16
Week 09 Oct 19 - Oct 25 Mid project presentations and global model interpretability Hw 09 due Fri Oct 23
Week 10 Oct 26 - Nov 01 Local model interpretability Hw 10 due Fri Oct 30
Week 11 Nov 02 - Nov 08 Foundations of deep learning Hw 11 due Fri Nov 06
Week 12 Nov 09 - Nov 15 Advanced topics in deep learning Hw 12 due Fri Nov 13
Week 13 Nov 16 - Nov 22 Final project presentations Hw 13 due Fri Nov 20
Week 14 Nov 23 - Nov 29 (Thanksgiving break; no class)
Week 15 Nov 30 - Dec 06 Final project presentations and consultation
Week 16 Dec 07 - Dec 13 (Final exam week; no class)

Homeworks

Resources

The course is designed to be self-contained and will often provide references for further details on various topics covered. The following references are wonderful resources for the material covered in this course:

Main textbooks: Supplementary resources: